Company
Technology
MachineLearningOperationsEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Machine Learning Operations Engineer. Skills: MLOps, Data Engineering, Infrastructure engineering. Design scalable infrastructure. Build scalable infrastructure”
What You'll Achieve.
Ensure production readiness; Ensure model health; Ensure system performance; Detect issues rapidly; Improve system architecture for scalability; Improve system architecture for reliability; Improve system architecture for uptime; Improve system architecture for cost efficiency
Industry & Context.
What They're Looking For.
Must Have
3+ years of experience in MLOps, Python and backend engineering principles, Deploying, monitoring, and maintaining ML models, Workflow orchestration tools such as Apache Airflow, Distributed data processing systems such as Kafka and Spark, Building and maintaining CI/CD pipelines, Cloud infrastructure and distributed system design, Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience, Communication and collaboration skills in cross-functional engineering teams, Proactive mindset with attention to detail, Focus on automation and reliability, Experience using AI tools to improve engineering productivity
Nice to Have
MLOps tools, frameworks, and best practices
What You'll Do.
Design scalable infrastructure
Build scalable infrastructure
Maintain scalable infrastructure
Deploy machine learning models
Monitor machine learning models
Manage machine learning models
Optimize ML pipelines
Implement continuous evaluation
Operationalize machine learning models
Ensure production readiness
Implement CI/CD pipelines
Support automated releases
Support reproducible releases
Build monitoring systems
Build logging systems
Build alerting systems
Ensure system performance
Detect issues rapidly
Improve system architecture
Integrate MLOps tools
Enhance platform capabilities
Document technical standards
Document operational procedures
Document architectural decisions
How You'll Work.
Team & Collaboration
Cross-functional engineering teams
Full Job Description
## Accountabilities Design, build, and maintain scalable infrastructure for deploying, monitoring, and managing machine learning models in production environments. Develop and optimize end-to-end ML pipelines, including feature engineering, model training workflows, deployment automation, and continuous evaluation systems. Collaborate with data scientists and product engineers to operationalize machine learning models and ensure production readiness. Implement and maintain CI/CD pipelines that support reliable, automated, and reproducible ML model releases. Build robust monitoring, logging, and alerting systems to ensure model health, system performance, and rapid issue detection. Improve system architecture for scalability, reliability, uptime, and cost efficiency in distributed environments. Research and integrate emerging MLOps tools, frameworks, and best practices to continuously enhance platform capabilities. Document technical standards, operational procedures, and architectural decisions to support engineering alignment and knowledge sharing. Requirements: 3+ years of experience in MLOps, Data Engineering, or infrastructure-focused software engineering roles. Strong proficiency in Python and backend engineering principles. Proven experience deploying, monitoring, and maintaining machine learning models in production environments. Hands-on experience with workflow orchestration tools such as Apache Airflow. Solid understanding of distributed data processing systems such as Kafka and Spark. Experience building and maintaining CI/CD pipelines for automated software and ML deployments. Strong understanding of cloud infrastructure and distributed system design. Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience. Strong communication and collaboration skills in cross-functional engineering teams. Proactive mindset with strong attention to detail and a focus on automation and reliability. Experience using AI tools to
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